LogoLogo
2024.2
  • Immuta Documentation - 2024.2
  • What is Immuta?
  • Self-Managed Deployment
    • Getting Started
    • Deployment Requirements
    • Install
      • Managed Public Cloud
      • Red Hat OpenShift
      • Generic Installation
      • Immuta in an Air-Gapped Environment
      • Deploy Immuta without Elasticsearch
    • Configure
      • Ingress Configuration
      • Cosign Verification
      • TLS Configuration
      • Immuta in Production
      • External Cache Configuration
      • Rotating Credentials
      • Enabling Legacy Query Engine and Fingerprint
    • Upgrade
      • Upgrade Immuta
      • Upgrade to Immuta 2024.2 LTS
    • Disaster Recovery
    • Troubleshooting
    • Conventions
    • Release Notes
  • Data and Integrations
    • Immuta Integrations
    • Snowflake
      • Getting Started
      • How-to Guides
        • Configure a Snowflake Integration
        • Snowflake Table Grants Migration
        • Edit or Remove Your Snowflake Integration
        • Integration Settings
          • Enable Snowflake Table Grants
          • Use Snowflake Data Sharing with Immuta
          • Configure Snowflake Lineage Tag Propagation
          • Enable Snowflake Low Row Access Policy Mode
            • Upgrade Snowflake Low Row Access Policy Mode
      • Reference Guides
        • Snowflake Integration
        • Snowflake Data Sharing
        • Snowflake Lineage Tag Propagation
        • Snowflake Low Row Access Policy Mode
        • Snowflake Table Grants
        • Warehouse Sizing Recommendations
      • Phased Snowflake Onboarding Concept Guide
    • Databricks Unity Catalog
      • Getting Started
      • How-to Guides
        • Configure a Databricks Unity Catalog Integration
        • Migrate to Unity Catalog
      • Databricks Unity Catalog Integration Reference Guide
    • Databricks Spark
      • How-to Guides
        • Configuration
          • Simplified Databricks Configuration
          • Manual Databricks Configuration
          • Manually Update Your Databricks Cluster
          • Install a Trusted Library
        • DBFS Access
        • Limited Enforcement in Databricks
        • Hide the Immuta Database in Databricks
        • Run spark-submit Jobs on Databricks
        • Configure Project UDFs Cache Settings
        • External Metastores
      • Reference Guides
        • Databricks Spark Integration
        • Databricks Spark Pre-Configuration Details
        • Configuration Settings
          • Cluster Policies
            • Python & SQL
            • Python & SQL & R
            • Python & SQL & R with Library Support
            • Scala
            • Sparklyr
          • Environment Variables
          • Ephemeral Overrides
          • Py4j Security Error
          • Scala Cluster Security Details
          • Databricks Security Configuration for Performance
        • Databricks Change Data Feed
        • Databricks Libraries Introduction
        • Delta Lake API
        • Spark Direct File Reads
        • Databricks Metastore Magic
    • Starburst (Trino)
      • Getting Started
      • How-to Guides
        • Configure Starburst (Trino) Integration
        • Customize Read and Write Access Policies for Starburst (Trino)
      • Starburst (Trino) Integration Reference Guide
    • Redshift
      • Getting Started
      • How-to Guides
        • Configure Redshift Integration
        • Configure Redshift Spectrum
      • Reference Guides
        • Redshift Integration
        • Redshift Pre-Configuration Details
    • Azure Synapse Analytics
      • Getting Started
      • Configure Azure Synapse Analytics Integration
      • Reference Guides
        • Azure Synapse Analytics Integration
        • Azure Synapse Analytics Pre-Configuration Details
    • Amazon S3
    • Google BigQuery
    • Legacy Integrations
      • Securing Hive and Impala Without Sentry
      • Enabling ImmutaGroupsMapping
    • Registering Metadata
      • Data Sources in Immuta
      • Register Data Sources
        • Create a Data Source
        • Create an Amazon S3 Data Source
        • Create a Google BigQuery Data Source
        • Bulk Create Snowflake Data Sources
      • Data Source Settings
        • How-to Guides
          • Manage Data Sources and Data Source Settings
          • Manage Data Source Members
          • Manage Access Requests and Tasks
          • Manage Data Dictionary Descriptions
          • Disable Immuta from Sampling Raw Data
        • Data Source Health Checks Reference Guide
      • Schema Monitoring
        • How-to Guides
          • Run Schema Monitoring and Column Detection Jobs
          • Manage Schema Monitoring
        • Reference Guides
          • Schema Monitoring
          • Schema Projects
        • Why Use Schema Monitoring?
    • Catalogs
      • Getting Started with External Catalogs
      • Configure an External Catalog
      • Reference Guides
        • External Catalogs
        • Custom REST Catalogs
          • Custom REST Catalog Interface Endpoints
    • Tags
      • How-to Guides
        • Create and Manage Tags
        • Add Tags to Data Sources and Projects
      • Tags Reference Guide
  • People
    • Getting Started
    • Identity Managers (IAMs)
      • How-to Guides
        • Microsoft Entra ID
        • Okta LDAP Interface
        • Okta and OpenID Connect
        • Integrate Okta SAML SCIM with Immuta
        • OneLogin with OpenID
        • Configure SAML IAM Protocol
      • Reference Guides
        • Identity Managers
        • SAML Single Logout
        • SAML Protocol Configuration Options
    • Immuta Users
      • How-to Guides
        • Managing Personas and Permissions
        • Manage Attributes and Groups
        • User Impersonation
        • External User ID Mapping
        • External User Info Endpoint
      • Reference Guides
        • Attributes and Groups in Immuta
        • Permissions and Personas
  • Discover Your Data
    • Getting Started
    • Introduction
    • Architecture
    • Data Discovery
      • How-to Guides
        • Enable Sensitive Data Discovery (SDD)
        • Manage Identification Frameworks
        • Manage Patterns
        • Manage Rules
        • Manage SDD on Data Sources
        • Manage Global SDD Settings
        • Migrate From Legacy to Native SDD
      • Reference Guides
        • How Competitive Pattern Analysis Works
        • Built-in Pattern Reference
        • Built-in Discovered Tags Reference
    • Data Classification
      • How-to Guides
        • Activate Classification Frameworks
        • Adjust Identification and Classification Framework Tags
        • How to Use a Built-In Classification Framework with Your Own Tags
      • Built-in Classification Frameworks Reference Guide
  • Detect Your Activity
    • Getting Started
      • Monitor and Secure Sensitive Data Platform Query Activity
        • User Identity Best Practices
        • Integration Architecture
        • Snowflake Roles Best Practices
        • Register Data Sources
        • Automate Entity and Sensitivity Discovery
        • Detect with Discover: Onboarding Guide
        • Using Immuta Detect
      • General Immuta Configuration
        • User Identity Best Practices
        • Integration Architecture
        • Databricks Roles Best Practices
        • Register Data Sources
    • Introduction
    • Audit
      • How-to Guides
        • Export Audit Logs to S3
        • Export Audit Logs to ADLS
        • Run Governance Reports
      • Reference Guides
        • Universal Audit Model (UAM)
        • Snowflake Query Audit Logs
        • Databricks Unity Catalog Audit Logs
        • Databricks Query Audit Logs
        • Starburst (Trino) Query Audit Logs
        • UAM Schema
        • Audit Export CLI
        • Governance Report Types
      • Deprecated Audit Guides
        • Legacy to UAM Migration
        • Download Audit Logs
        • System Audit Logs
    • Detection
      • Use the Detect Dashboards
      • Reference Guides
        • Detect
        • Detect Dashboards
        • Unknown Users in Audit Logs
    • Monitors
      • Manage Monitors and Observations
      • Detect Monitors Reference Guide
  • Secure Your Data
    • Getting Started with Secure
      • Automate Data Access Control Decisions
        • The Two Paths: Orchestrated RBAC and ABAC
        • Managing User Metadata
        • Managing Data Metadata
        • Author Policy
        • Test and Deploy Policy
      • Compliantly Open More Sensitive Data for ML and Analytics
        • Managing User Metadata
        • Managing Data Metadata
        • Author Policy
      • Federated Governance for Data Mesh and Self-Serve Data Access
        • Defining Domains
        • Managing Data Products
        • Managing Data Metadata
        • Apply Federated Governance
        • Discover and Subscribe to Data Products
    • Introduction
      • Scalability and Evolvability
      • Understandability
      • Distributed Stewardship
      • Consistency
      • Availability of Data
    • Authoring Policies in Secure
      • Authoring Policies at Scale
      • Data Engineering with Limited Policy Downtime
      • Subscription Policies
        • How-to Guides
          • Author a Subscription Policy
          • Author an ABAC Subscription Policy
          • Subscription Policies Advanced DSL Guide
          • Author a Restricted Subscription Policy
          • Clone, Activate, or Stage a Global Policy
        • Reference Guides
          • Subscription Policies
          • Subscription Policy Access Types
          • Advanced Use of Special Functions
      • Data Policies
        • Overview
        • How-to Guides
          • Author a Masking Data Policy
          • Author a Minimization Policy
          • Author a Purpose-Based Restriction Policy
          • Author a Restricted Data Policy
          • Author a Row-Level Policy
          • Author a Time-Based Restriction Policy
          • Certifications Exemptions and Diffs
          • External Masking Interface
        • Reference Guides
          • Data Policy Types
          • Masking Policies
          • Row-Level Policies
          • Custom WHERE Clause Functions
          • Data Policy Conflicts and Fallback
          • Custom Data Policy Certifications
          • Orchestrated Masking Policies
    • Domains
      • Getting Started with Domains
      • Domains Reference Guide
    • Projects and Purpose-Based Access Control
      • Projects and Purpose Controls
        • Getting Started
        • How-to Guides
          • Create a Project
          • Create and Manage Purposes
          • Adjust a Policy
          • Project Management
            • Manage Projects and Project Settings
            • Manage Project Data Sources
            • Manage Project Members
        • Reference Guides
          • Projects and Purposes
          • Policy Adjustments
        • Why Use Purposes?
      • Equalized Access
        • Manage Project Equalization
        • Project Equalization Reference Guide
        • Why Use Project Equalization?
      • Masked Joins
        • Enable Masked Joins
        • Why Use Masked Joins?
      • Writing to Projects
        • How-to Guides
          • Create and Manage Snowflake Project Workspaces
          • Create and Manage Databricks Project Workspaces
          • Write Data to the Workspace
        • Reference Guides
          • Project Workspaces
          • Project UDFs (Databricks)
    • Data Consumers
      • Subscribe to a Data Source
      • Query Data
        • Querying Snowflake Data
        • Querying Databricks Data
        • Querying Databricks SQL Data
        • Querying Starburst (Trino) Data
        • Querying Redshift Data
        • Querying Azure Synapse Analytics Data
      • Subscribe to Projects
  • Application Settings
    • How-to Guides
      • App Settings
      • BI Tools
        • BI Tool Configuration Recommendations
        • Power BI Configuration Example
        • Tableau Configuration Example
      • Add a License Key
      • Add ODBC Drivers
      • Manage Encryption Keys
      • System Status Bundle
    • Reference Guides
      • Data Processing, Encryption, and Masking Practices
      • Metadata Ingestion
  • Releases
    • Immuta v2024.2 Release Notes
    • Immuta Release Lifecycle
    • Immuta LTS Changelog
    • Immuta Support Matrix Overview
    • Immuta CLI Release Notes
    • Immuta Image Digests
    • Preview Features
      • Features in Preview
    • Deprecations
  • Developer Guides
    • The Immuta CLI
      • Install and Configure the Immuta CLI
      • Manage Your Immuta Tenant
      • Manage Data Sources
      • Manage Sensitive Data Discovery
        • Manage Sensitive Data Discovery Rules
        • Manage Identification Frameworks
        • Run Sensitive Data Discovery on Data Sources
      • Manage Policies
      • Manage Projects
      • Manage Purposes
    • The Immuta API
      • Integrations API
        • Getting Started
        • How-to Guides
          • Configure an Amazon S3 Integration
          • Configure an Azure Synapse Analytics Integration
          • Configure a Databricks Unity Catalog Integration
          • Configure a Google BigQuery Integration
          • Configure a Redshift Integration
          • Configure a Snowflake Integration
          • Configure a Starburst (Trino) Integration
        • Reference Guides
          • Integrations API Endpoints
          • Integration Configuration Payload
          • Response Schema
          • HTTP Status Codes and Error Messages
      • Immuta V2 API
        • Data Source Payload Attribute Details
        • Data Source Request Payload Examples
        • Create Policies API Examples
        • Create Projects API Examples
        • Create Purposes API Examples
      • Immuta V1 API
        • Authenticate with the API
        • Configure Your Instance of Immuta
          • Get Fingerprint Status
          • Get Job Status
          • Manage Frameworks
          • Manage IAMs
          • Manage Licenses
          • Manage Notifications
          • Manage Sensitive Data Discovery (SDD)
          • Manage Tags
          • Manage Webhooks
          • Search Filters
        • Connect Your Data
          • Create and Manage an Amazon S3 Data Source
          • Create an Azure Synapse Analytics Data Source
          • Create an Azure Blob Storage Data Source
          • Create a Databricks Data Source
          • Create a Presto Data Source
          • Create a Redshift Data Source
          • Create a Snowflake Data Source
          • Create a Starburst (Trino) Data Source
          • Manage the Data Dictionary
        • Manage Data Access
          • Manage Access Requests
          • Manage Data and Subscription Policies
          • Manage Domains
          • Manage Write Policies
            • Write Policies Payloads and Response Schema Reference Guide
          • Policy Handler Objects
          • Search Audit Logs
          • Search Connection Strings
          • Search for Organizations
          • Search Schemas
        • Subscribe to and Manage Data Sources
        • Manage Projects and Purposes
          • Manage Projects
          • Manage Purposes
        • Generate Governance Reports
Powered by GitBook

Other versions

  • SaaS
  • 2024.3

Copyright © 2014-2024 Immuta Inc. All rights reserved.

On this page
  • Column detection
  • Tracking new data sources and columns
  • Workflow
  • Performance
  • Schema monitoring for Databricks
  • Schema monitoring for Snowflake
  • Architecture
  • Automatic workflow
  • Limitations
  • Configuration
  • Schema monitoring best practices

Was this helpful?

Export as PDF
  1. Data and Integrations
  2. Registering Metadata
  3. Schema Monitoring
  4. Reference Guides

Schema Monitoring

PreviousReference GuidesNextSchema Projects

Last updated 3 months ago

Was this helpful?

Schema monitoring allows organizations to monitor their data environments. When it is enabled, Immuta monitors the organization's servers to detect when new tables or columns are created or deleted, and automatically registers (or disables) those tables in Immuta. These newly updated data sources will then have any global policies and tags that are set in Immuta applied to them. The Immuta data dictionary will be updated with any column changes, and the Immuta environment will be in sync with the organization's data environment. This automated process helps organizations keep compliant without the need to manually keep data sources up to date.

Schema monitoring is enabled while creating or editing a data source and only registers new tables and columns within known schemas. It does not register new schemas. Data owners or governors can edit the naming convention for newly detected data sources and the schema detection owner from the after it has been enabled.

See the guides for instructions on enabling schema monitoring or for instructions on editing the schema monitoring settings.

Column detection

Column detection is a part of schema monitoring, but can also be enabled on its own to detect the column changes of a select group of tables. Column detection monitors when columns are added or removed from a table and when column types are changed and updates those changes in the appropriate Immuta data source's data dictionary.

See the guide for instructions on enabling column detection.

Tracking new data sources and columns

When new data sources and columns are detected and added to Immuta, they will automatically be tagged with the New tag. This allows governors to use the seeded New Column Added global policy to mask the data sources and columns, since they could contain sensitive data. Data owners can then review and approve these changes from the requests tab of their profile page. Approving column changes removes the New tags from the data source.

The New Column Added global policy is staged (inactive) by default.

See the to activate this seeded global policy if you want any new columns to be automatically masked.

Workflow

  1. Immuta user with schema monitoring enabled.

  2. Every 24 hours, at 12:30 a.m. UTC by default, Immuta checks the servers for any changes to tables and columns.

  3. If Immuta finds a change, it will update the appropriate Immuta data source or column:

    1. If Immuta finds a new table, then Immuta creates an Immuta data source for that table and tags it New.

    2. If Immuta finds a table has been deleted, then Immuta disables that table's data source.

    3. If Immuta finds a previously deleted table has been re-created, then Immuta restores that table's data source and tags it New.

    4. If Immuta finds a new column within a table, then Immuta adds that column to the data dictionary and tags it New.

    5. If Immuta finds a column has been deleted, then Immuta deletes that column from the data dictionary.

    6. If Immuta finds a column type has changed, then Immuta updates the column type in the data dictionary.

  4. Data sources and columns tagged New will be masked by the seeded New Column Added global policy until a governor or data owner approves the changes.

Performance

The default schedule for schema monitoring to run is every 24 hours at midnight. Some organizations may need to schedule it to run more often; however, this could have negative performance impacts.

Schema monitoring for Databricks

Schema monitoring for Snowflake

Immuta can monitor your data environment, detect when new tables or columns are created or deleted in Snowflake, and automatically register (or disable) those tables in Immuta for you. Those newly updated data sources will then have any global policies and tags that you have set up applied to them. The Immuta data dictionary will be updated with any new columns, and your Immuta environment will be in sync with your Snowflake tables. This automated process helps with scaling and keeping your organization compliant without the need to manually keep your data sources up to date.

Architecture

Once enabled on a data source, Immuta calls to Snowflake every 24 hours by default to find when each table within the registered schema was last altered. If the timestamp is after the last time schema monitoring was run, then Immuta will update the table or columns that have been altered. This process works well when monitoring a large number of data sources because it only updates the recently altered tables and cuts down the amount of Snowflake computing required to run column detection, which specifically updates the columns of registered data sources.

Automatic workflow

  1. Every 24 hours, at 12:30 a.m. UTC by default, Immuta sends a query to Snowflake for the information_schema view asking for when each data source’s table was last altered. To adjust these settings, reach out to your Immuta representative.

  2. If the table was altered after the last time schema monitoring ran, Immuta updates the data source, columns, and data dictionary.

  3. Immuta tags new data sources and columns with the tag “New” so that you can use the templated "New Column Added" global policy to mask all new data until it has been reviewed.

Limitations

  • This feature only works with Snowflake data sources. Any non-Snowflake data sources will run with the legacy schema monitoring described above.

  • Your organization will not see performance improvements if it is making changes to all tables consistently. This feature is intended to improve performance for organizations with a large number of tables and relatively few changes made within the ecosystem comparatively.

Configuration

There is no additional configuration required for this feature. You just need to enable schema monitoring when you create your Snowflake data sources.

Schema monitoring best practices

To run schema monitoring or column detection manually, see the .

In most cases, Immuta’s schema monitoring job runs automatically from the Immuta web service. For Databricks, that automatic job is disabled because of the . In this case, Immuta requires users to download a schema detection job template (a Python script) and import that into their Databricks workspace. See the guide for details.

Immuta user .

Manually trigger schema monitoring (filtered down to the database) after your dbt or other transform workflows run. For more information, see the .

When manually triggering schema monitoring, specify a table or database for maximum performance efficiency and to reduce data or policy downtime. For more information on triggering schema monitoring, see the .

If you are manually managing data tags, activate the to protect newly found and potentially sensitive data. This policy sets all new columns to NULL until a data owner reviews the new columns. Using this workflow protects your data and avoids data leaks on new columns getting automatically added. This recommendation is unnecessary for users leveraging sensitive data discovery (SDD) or using an external data catalog.

Run schema monitoring and column detection jobs page
dbt and transform workflow for limited policy downtime guide
schema project page
Register a data source
Manage schema monitoring
Register a data source
registers a data source
ephemeral nature of Databricks clusters
Manually run schema monitoring guide
Clone, activate, or stage a global policy guide
"New Column Added" global policy
Register a Databricks data source
creates a data source with schema monitoring enabled
If necessary, an Immuta admin can also manually run schema monitoring through the API to run globally on all data sources.